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Static maps are the most common type of visual output from geocomputation. Standard formats include .png and .pdf for raster and vector outputs respectively. Initially, static maps were the only type of maps that R could produce.
- 10 Scripts, Algorithms and Functions
10.1 Introduction. Chapter 1 established that geocomputation...
- 9 Bridges to GIS Software
Chapter 9 Bridges to GIS software | Geocomputation with R is...
- 11 Statistical Learning
11.3 Conventional modeling approach in R. Before introducing...
- 4 Spatial Data Operations
Spatial operations also apply to raster objects. Spatial...
- 6 Reprojecting Geographic Data
Chapter 6 Reprojecting geographic data | Geocomputation with...
- 2 Geographic Data in R
Chapter 2 Geographic data in R | Geocomputation with R is...
- Preface
Preface | Geocomputation with R is for people who want to...
- References
References | Geocomputation with R is for people who want to...
- 10 Scripts, Algorithms and Functions
Create maps in ggplot2 from shapefiles or geojson files using the geom_sf, geom_polygon or geom_map functions and change the coordinate systems with coord_sf. In addition, learn how to create interactive maps with ggplotly.
This vignette describes the R package raster. A raster is a spatial (geographic) data structure that divides a region into rectangles called “cells” (or “pixels”) that can store one or more values for each of these cells.
31 mar 2015 · In this post we show some simple (and not-so-simple) examples of how to work with raster data in R with a focus on the raster package. This post also makes extensive use of the “new” R workflow with the packages dplyr , magrittr , tidyr and ggplot2 .
Vector to raster conversion ¶. The raster package supports point, line, and polygon to raster conversion with the rasterize function. For vector type data (points, lines, polygons), objects of Spatial* classes defined in the sp package are used; but points can also be represented by a two-column matrix (x and y).
Vector and raster data models are two basic models used to represent spatial data. These spatial data models are closely related to map making, with each model having its own pros and cons. This chapter stars by describing several popular spatial data models (section 2.2).
In this episode, we will introduce the fundamental principles, packages and metadata/raster attributes that are needed to work with raster data in R. We will discuss some of the core metadata elements that we need to understand to work with rasters in R, including CRS and resolution.
